Wireless tags used for wireless radio-signaling, such as radio frequency identification (RFID) tags or labels, can include memory to store data that identifies a wireless tag, as well as other information about an object that the wireless tag may be associated with, such as when attached to the object or included in object packaging. Conventional systems used for localizing a RFID tag, such as used to locate an associated object, involves using multiple stationary RFID readers with high power antennas to triangulate the position of the wireless tag. This type of RFID tracking system is not cost effective to implement, difficult to scale, and requires complex setup.
Similarly, conventional augmented reality (AR) systems are difficult to implement due to operating in the visual domain, needing a line-of-sight to ascertain a real object and the position of the object in an augmented reality environment. For example, an object that is recognizable from a front view, as determined by an AR system, may not be recognized by the system from a side, back, or top view of the object. This type of visual object detection utilizes a great deal of processing power and requires system training to visually recognize an object from multiple perspectives. Additionally, an AR system is subject to other visual-based challenges, such as occlusion and poor lighting that can limit recognizing the features of an object.